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Author(s)
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Issue Date
Built Environment or Household Life-Cycle Stages̶Which
Explains Sustainable Travel More?
Sun, Yilin; Waygood, E. Owen D.; Fukui, Kenichiro;
Kitamura, Ryuichi
Transportation Research Record: Journal of the Transportation
Research Board (2009), 2135: 123-129
2009-12-01
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http://hdl.handle.net/2433/128872
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© 2009 National Academy of Sciences; この論文は出版社版
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ださい。This is not the published version. Please cite only the
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Journal Article
Textversion
author
Kyoto University
Sun, Waygood, Fukui, and Kitamura
THE BUILT ENVIRONMENT OR HOUSEHOLD LIFECYCLE STAGES: WHICH
EXPLAINS SUSTAINABLE TRAVEL MORE? THE CASE OF KYOTO-OSAKAKOBE BUILT AREA
Yilin Sun1
[email protected]
E. Owen D. Waygood1
[email protected]
Kenichiro Fukui2
[email protected]
Ryuichi Kitamura3
[email protected]
1 PhD Candidate
Department of Urban Management
Kyoto University
C Cluster Katsura Campus
Nishikyo-ku, Kyoto, Japan
P: +81-75-383-3242
F: +81-75-383-3236
2 Urban Transportation Planner
Urban Transportation Planning Dept.,
Hankyu Corporation
1-16-1 Shibata Kita-ku Osaka
530-8389 JAPAN
P: +81-6-6373-5031
3 Professor
Department of Urban Management
Kyoto University
C Cluster Katsura Campus
Nishikyo-ku, Kyoto, Japan
P: +81-75-383-3238
F: +81-75-383-3241
Word Count: 3822 + 3 Figures + 6 Tables = 5822
1
Sun, Waygood, Fukui, and Kitamura
2
ABSTRACT
Sustainable travel is a goal deserving of research and implementation, but how such a goal
can be reached is debated. Fueling this debate are the many different factors involved in
individual travel ranging from values and beliefs to the impact of the built environment. The
amount of impact that the built environment may have can be clouded by a person’s personal
preference for a certain lifestyle and different lifecycle stages have different levels of travel.
Although low levels of automobile use have been observed in city centers, the question
remains as to whether the demographics of the distinct developed areas can explain the
differences. This paper investigated the fraction of automobile trips across different
developed areas for households of distinct lifecycle stages to determine which explained the
differences greater. The results suggest that it is the built environment that has a greater
ability to explain the differences in the fraction of automobile trips and that households of the
same lifecycle stage retain the same basic number of trips.
Sun, Waygood, Fukui, and Kitamura
3
INTRODUCTION
Sustainable travel has many aspects to it, but a dominant theme is the reduction of energy
required to accomplish trips. In Japan, a study on per passenger energy use for different
motorized modes found that passenger cars used nearly twice that of city bus passengers and
over six times that of train passengers per kilometer (1). In many countries, a general increase
in the share of automobile trips has been documented (2), suggesting a corresponding
increase in energy use. However, given a built environment that facilitates all modes, the
percentage of trips that are by an automobile may be small despite increases in wealth and
automobile ownership, but it has been difficult to distinguish the role of the built environment
on travel behavior. Creating more sustainable travel will require changing people’s behavior
and changing the built environment is just one aspect of that.
Changing people’s behavior is a difficult thing. Education to change behavior may
work in a situation where the desired behavior already exists and only needs reinforcement.
In a situation where the change in behavior is difficult because of factors such as limited
choice or psychological barriers such as group behavior, behavior change may require
stronger guidance than education such as laws. Group behavior is the tendency for humans to
follow the majority’s actions and conversely, for the group to seek compliance from
individuals. Increasing laws to restrict certain behavior may not be politically feasible if they
go against the majority’s behavior or social norms. In terms of sustainable travel, education
to increase the use of modes other than private automobiles may work if there is the
appropriate infrastructure and supporting features such as local stores and safe routes for nonmotorized modes. But in situations where that does not exist, travel behavior change may be
more difficult. Changing the built environment is a major step and questions concerning the
extent of its effect on travel behavior still exist.
Changing the built environment has been suggested as a method to reduce automobile
use (3) and some studies have shown a difference in travel behavior between neighborhoods
built prior to 1945 and those built after (4). On the other hand, some authors assert that
individual and household circumstances have greater influence over travel behavior than do
land use patterns (5). Personal preference may also contribute to differences seen between
neighborhoods. If people who prefer to walk or cycle seek out neighborhoods where they feel
comfortable doing so, the impact of the built environment may be exaggerated. This is
termed ―residential self-selection‖ and is discussed further below. Other research on the same
group of people before and after a move suggest that travel behavior may be especially
entrenched for private automotive vehicle users (6, 7) and that the built environment has low
explanatory power.
Residential self-selection is a factor that can affect the differences seen between
neighborhood styles. Cao et al. (8) covers this topic well and summarizes that the built
environment does have its own distinct influence on travel behavior separate from that of
residential self-selection. Although this paper does not specifically address characteristics of
neighborhoods such as sidewalk availability (though it should be noted that sidewalks are not
prevalent in the study area, typically only existing by major roads) or parking availability, it
does distinguish the developed areas according to diurnal population changes, and the relative
amount of commercial development and employment available locally. Having locally
available employment reduces the distance required to commute, which can facilitate nonmotorized modes of travel more easily.
Certainly personal preference will contribute to travel behavior, but can it explain the
extreme differences seen between major centers such as the Kei-Han-Shin area (abbreviation
for an area encompassing Kyoto, Osaka, and Kobe) and Los Angeles (9). Without a doubt,
culture plays some part in the differences, but then similar differences in travel behavior can
increasingly be seen within the Kei-Han-Shin area between different development styles.
Sun, Waygood, Fukui, and Kitamura
4
Japan has developed a built environment often centered around a train station and
with mixed land-use, two aspects of Transit Oriented Design (TOD). Although it is not free
of increased automobility, the choice to do shopping trips locally is still possible and a large
percentage (roughly 60%) is by non-motorized modes (10) in the Kei-Han-Shin area.
However, not all areas have developed in this TOD-like method and suburban areas have
seen a greater increase in total trip energy consumption than urban areas (9). For that study,
the Kei-Han-Shin area was divided into 194 geographical areas and classified as urban,
suburban, and unurbanized (10).
One possible explanation for this increasing difference is the household makeup of
each area. It could be that older people are remaining in the city, continuing in their
―entrenched‖ behavior, and that young families are establishing themselves in less developed
areas. If however, households of the same lifecycle stages were compared across different
developed areas, then the impact of the built environment may become evident.
Unfortunately, personal preference may still rear its head here. People may choose to
live in a certain area because of lifestyle preferences. Living in an area where easier
movement by private vehicle is possible may allow for a greater number of trips or perhaps
less time to accomplish tasks. Therefore, it is important to consider the number of trips. If the
number of trips were similar within households of the same lifecycle stage across different
developed areas, then it could be surmised that they have similar levels of activity.
This paper will examine the concepts discussed above by first explaining how the
different built environments were identified for the Kei-Han-Shin area. Then, to look at
changes in automobile use across the different built environments, repeated cross-sectional
data were used from 1970, 1980, 1990, and 2000 to examine the change in the fraction of
automobile trips over time across the different areas. Distinct lifecycle stages for each
household were identified and cross-sectional data for the year 2000 were used to investigate
if differences between the built environments remained. The same data were then used to
determine the average number of household trips, and then averages are compared in order to
consider lifestyle differences.
STUDY AREA AND CHARACTERISTICS
The study was completed in the Kyoto-Osaka-Kobe developed area of western Japan. The
population was 18.2 million and the area 9,223 km2, which results in a population density of
1975 people/km2 (5115 people/m2). The developed area was characterized by mixed land-use
in all areas. Although the urban areas are quite developed, suburbanization exists. However,
the style of suburbanization is different from typical North American suburban developments
in that shops and services may still exist within neighborhoods and train stations are present
and are often used by commuters.
The national average of household car ownership in Japan was 1.12, but the study
area had an average of 0.97. It varied from 0.5 in the urban centers to 1.37 in the lower
density developments. Private vehicles did not necessarily offer a significant mobility
advantage in the urban areas where shops were nearby and roads were narrow with a high
number of users, both automotive and non-automotive.
The area has a long history of rail development with urban rail being established in
the main cities before 1900. Although some of the rail lines were previously nationally owed,
all rail lines in the area are presently privately operated. Residential areas have traditionally
developed around train stations, though this trend has somewhat changed in the past couple
of decades.
It should also be emphasized that although zoning does exist in Japan, it is not nearly
as strict as seen in North America. Even in the strictest residential zoning from the 1968 City
Planning Law, multi-dwelling development, stores, and offices area allowed (11). Prior to
Sun, Waygood, Fukui, and Kitamura
5
this law, even less strict zoning applied from the 1919 City Planning Law (for a detailed
description of the development of Japanese urbanization, please see Sorenson (11)). As a
result, practically all urban areas could be termed mixed land-use. This may lead to lower
car-dependency than seen in more strict zoning development.
The data used to determine lifecycle stages came from large-scale household travel
surveys which are conducted every 10 years since 1970. The data used to distinguish
different kinds of developed areas is shown later in the paper.
There were two main stages to this analysis. The first examined the study area and
used available information to define different developed areas. The second method used
socio-demographic information from a repeated cross-sectional travel survey to determine the
lifecycle stage of each household. These two stages are described in detail below.
IDENTIFYING DIFFERENT BUILT ENVIRONMENTS
This section gives details about how the distinct developed areas were determined. The areas’
boundaries were determined by political boundaries established by the Japanese government.
A considerable amount of information about the residences’ characteristics, the densities of
both people and shops, along with the employment situation was used to identify the different
developed areas. The different divisions of information that were considered, the specific
factors, and their sources are shown in Table 1.
Analysis on these factors was done using cluster analysis and five basic developed areas
were identified: commercial, mixed commercial-residential, mixed residential, autonomous,
and emerging. The resulting divisions are shown in Table 2. The basic definitions of each
area are:
1) A highly commercial mixed area is an area with the highest densities of commercial
development. These areas have a high day-time population with respect to the night-time
population.
2) Mixed commercial areas have a high density of commercial development, though not as
high as a commercial area and have residential development as well. There is a less
distinct change in the day- and night- populations.
3) Mixed residential area does not have sufficient work for the population and some
residents must commute to another area. These areas are distinguished by a higher nighttime population than day-time. However, these areas can have high population densities
which support high store and service densities.
4) Autonomous areas have a roughly equal amount of residential and commercial
development which allows residents to live and work within the area. There is not
significant change in the day- and night-time populations. However, the population
density is low and these areas are located quite far from major urban centers.
5) Unurbanized areas have low densities of both commercial and residential development
and low employment opportunities.
The average characteristic values for each area are shown in Table 3.
LIFECYCLE STAGES
The strengths and weaknesses of the lifecycle concept in travel behavior have been discussed
in many travel research papers, and these studies point to the presence of a lifecycle effect in
travel behavior. Heggie (12) found evidence for the significance of family structure in an
exploratory study of the reactions of Oxford residents to that city’s policy of car restraint. He
found that many of the reported responses were the results of behavior which was strongly
constrained by family circumstances – these constraints being of a different nature depending
on the numbers and ages of the children in the family. Jones et al. (13) used lifecycle stage as
a key classificatory variable. Zimmerman (14) showed differences in the average daily trip
Sun, Waygood, Fukui, and Kitamura
6
frequency across households of different lifecycle stages; e.g., single parents and nuclear
families show increases in trip-making as the household head becomes older. Clarke et al.
(15) developed micro-analytic simulation models of travel behavior. They assess the
implications for travel given various combinations of probabilities, through ageing a
hypothetical population through various household types and lifecycle stages, and simulating
the impact of demographic, socioeconomic, and location variables at each stage.
However, what these studies have argued about the application of the lifecycle
concept in transportation planning is only beginning to be realized, and the concept has been
used uncritically and simplistically. This paper considers not only the impact of lifecycle, but
also the impact of the built environment for the fraction of all trips that are by private
automobile and also the number of trips. Ten distinct stages of lifecycle were formulated as
shown in Table 4.
ANALYSIS
Analysis of variance (ANOVA) was used to determine if the differences observed were
statistically different for each separate analysis. ANOVA was completed for the fraction of
automobile trips by all households across the previously defined developed areas for the
years 1970, 1980, 1990, and 2000. It was then used to determine the differences between
each lifecycle stage across each developed area for the year 2000. The final analysis was
completed for the total number of household trips for each lifecycle stage across each
developed area for the year 2000. All results were statistically valid at p < 0.001 and the
number of respondents was 146,820 for the year 2000.
RESULTS AND DISCUSSION
Different built environments were identified for the Kei-Han-Shin area of Japan. Commercial
and mixed commercial built environments were located at the ―centers‖ of the three main
cities of Kyoto, Osaka, and Kobe. Mixed residential was often just outside the centers of the
main cities. The remaining areas were classified as autonomous or emerging depending on
the constraints discussed in the Identifying Different Built Environments section.
Increasing Fractions of Automobile Use
In all areas except for the commercial and mixed-commercial areas, a significant growth in
the fraction of household trips completed by automobile can be seen in Figure 1. From these
results, it appears that the more densely developed built environments, commercial and
mixed-commercial, had a limiting effect on the fraction of automobile trips. However,
without knowing how households of different lifecycle stages behave within each of those
areas, it could be argued that the same people are continuing to live in the commercial and
mixed-commercial areas, and that their behavior is simply entrenched.
It is interesting to note that even in the most extreme cases, the fraction of household
travel is roughly 50%. Speculatively, this may be a result of mixed land-use in all areas.
Lifecycle Stages within Different Built Environments
In the last sub-section we saw that the rate of increase of the fraction of automobile trips was
different for each built environment, but the types of households that live in each area may
affect that result. This sub-section shows the results of comparing the different lifecycle
stages described in the Identifying Different Built Environments section across each built
environment for the year 2000.
Figure 2 shows the fraction of automobile trips for each lifecycle stage within each
built environment. The trend is that the fraction of automobile trips is related to the built
environment, and less to the lifecycle stage. This result would suggest that the built
Sun, Waygood, Fukui, and Kitamura
7
environment and not the household type is a stronger explainer of the fraction of automobile
trips.
A two variable ANOVA analysis (Table 5) of the built environment and lifecycle
stage confirms that the built environment rather than lifecycle stages is a significant explainer
for the fraction of automobile trips for a household. From that table, it can be seen that the
portion explained by the built environment (Sum of Squares) is larger than either the lifecycle
stage or the combined effect of the two.
However, there may still be variance between lifestyles within each lifecycle stage.
Therefore, the number of trips that each household of the different lifecycle stages completes
over a day will be compared. The number of trips may be an indication of how often the
person engages in activities outside their home.
Number of Trips by Households of the Same Lifecycle Stage across Different Built
Environments
The number of trips completed by a household is used here as a proxy for how often the
household engages in activities outside the home. These results may show if there are
significant differences in lifestyles (e.g. a high number of trips may suggest a highly active
and engaged person, while a low number of trips may suggest a more sedentary life).
The results can be seen in Figure 3 which shows that there is very little difference
across the built environments, but that differences can be seen across the lifecycle stages.
This suggests that households of same lifecycle stage are completing a similar number of
trips per day, no matter the built environment, and that there are distinct differences between
lifecycle stages. This result in combination with the Figure 2 would suggest that the built
environment will determine the fraction of automobile trips, and that the differences seen in
Figure 1 are not a result of different household lifecycle or lifestyle with respect to the
number of trips being made.
Land Use and Travel Behavior in this Study
This paper showed that there were underlying travel patterns for different built environments
in the Kei-Han-Shin area of Japan with respect to the fraction of automotive trips per
household (Figure 1). It showed that within the same lifecycle stages, that a similar difference
could be seen amongst the different built environments (Figure 2). Lastly, it showed that the
number of trips per household within the same lifecycle stage did not have extreme variances
(Figure 3), implying similar levels of activity. The graphs are not household specific, but
population-wide averages for the differentiated built environments, but Table 5 shows
household-level results. All areas allowed for mixed land-use, though greater urbanization
may facilitate greater diversity through increased population densities. This less strict zoning
may contribute to the less than 50% fraction for lifecycle stages with children in all areas. It
should be noted that children’s travel in Japan is highly non-motorized (16) which could
account for that result.
The different lifecycle stages clearly show different total numbers of trips, but it was
the difference in the residential locations that showed different travel behavior with respect to
automobile use. Further, research conducted in this same study area showed that structural
changes were the most significant contributor to energy consumption over the period 1970 to
2000 (10). In contrast to the Giuliano and Hanson statement that household circumstances are
a stronger influence than land use on travel patterns (5), these two results both suggest that
the land use as an aspect of the built environment is perhaps more significant a determinant
of how people will travel, while lifecycle stage predicts the number of trips that a household
conducts. Although household income was not directly addressed in this paper, it should be
noted that households of different income levels are highly mixed in the study area. What
Sun, Waygood, Fukui, and Kitamura
8
may be the case is that in an auto-oriented development area, the increase in trips required by
an expanding family must be conducted by automobile. A wealthier family will have a
greater ability to supply vehicles and to have the extra funds to participate in activities. On
the other hand, in higher density, mixed land-use areas those extra trips can be completed by
non-motorized modes or public transit. As noted above, children’s travel during the week is
highly non-motorized and independent of parents for more urban areas (16) which would
support that assertion.
Society Influences Our Behavior
Human beings are social creatures and our instincts encourage us to follow general group
behavior, called normative social influence. Although this tendency is stronger in certain
cultures (17) it may help explain why in a study on the impact of different public transport
service levels, no difference was observed in travel behavior in Los Angeles (9), which is
dominated by private vehicle use. Conversely, as was mentioned in the introduction, roughly
60% of shopping trips are still done by non-motorized modes in the Kei-Han-Shin area of
Japan, which likely contributes to the continued use of these modes over private vehicles
despite an increase in ownership, as walking and cycling are the social norm. Unfortunately,
as walking and cycling are not the social norm in North America, someone who is seen
traveling by such modes will be subconsciously viewed with suspicion as evolution has
taught us to see the non-group behavior as a threat (17).
To overcome these tendencies in humans, several things must change. Transit
Demand Management (TDM) practices such as Voluntary Travel Behavior Change (VTBC)
which work to overcome mental effort barriers can make changes to people’s thinking and
behavior (18), but to achieve greater change, the built environment must also change. This
paper showed even when comparing households of the same lifecycle stage, that the built
environment was a stronger explainer for the fraction of automotive trips. The potential for
change is therefore likely greater with a built environment conducive to sustainable travel
than conscientious individuals who are in a car-oriented built environment. Once the built
environment has changed and more sustainable forms of travel are increasingly more possible,
education such as VTBC may be more effective in changing overall behavior. Litman and
Steele (3) give numerous examples of the more sustainable travel seen in mixed land-use
development over mono-use areas. As well, medical research is now looking to the built
environment as a way to improve physical health and results reflect those seen for
environmentally improved development (19, 20, 21).
Development patterns such as Smart Growth (22) propose how compact cities, TOD,
and TDM can all combine to improve livability and sustainability of the urban built
environment. As discussed above, the study area reflects many of the design principles
proposed such as transit-based development, mixed land-use, and greater community
cohesion (10, 16). This paper showed that areas with the highest population densities and
commercial land use have had relatively low use of automobiles, even as increased
motorization was seen in other less urban areas. In terms of energy use and health through
non-motorized travel, those areas are more sustainable.
In democratic societies, it may be necessary to convince the voting public of the
benefits of changing to development such as Smart Growth before they could occur. But with
few examples of good, high-density, mixed land-use developments remaining in North
America, it may be difficult for most people to imagine anything different than what
currently exists. The hardest task may now be not proving the benefits of TOD-like
development to researchers, but to the general public. Greater efforts are needed in the
dissemination of such knowledge through media such as newspapers and television.
Sun, Waygood, Fukui, and Kitamura
9
CONCLUSIONS
This paper investigated how the built environment may create environments where more
sustainable travel is possible by considering the fraction of automobile trips across different
developed areas and within distinct lifecycle stages. The results suggest that the built
environment has significant correlation with the fraction of automobile trips even when
households of different lifecycle stages are compared. No significant differences were
observed in the number of trips for households of the same lifecycle stage across different
built environments, suggesting that similarly active lifestyles exist. These results suggest that
significantly more sustainable behavior for society would be possible with more compact
built environments that facilitate non-motorized and public transit travel. Unfortunately, it
takes time, money, resources, and the political will to change the built environment and initial
steps that educate the public such as voluntary travel behavior change may be necessary first
steps on the move to more sustainable travel.
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metropolitan area. Journal of the City Planning Institute of Japan, Vol. 35, 2000, pp. 469-74,
(in Japanese).
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related to desirable urban form. Advances in Transport, Vol. 8; urban transport VII: Urban
transport & the environment in the 21st C, 2001, pp. 133-144
3. Litman, T. and R. Steele. Land Use Impacts on Transport: How Land Use Factors Affect
Travel Behavior. Victoria Transport Policy Institute
4. Handy, S. Methodologies for Exploring the link b/w urban form and travel behavior.
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3rd ed. Edited by S. Hanson and G. Giulinao, 2004, pp. 382-403
6. Waygood, E.O.D. and R. Kitamura. How Changing the Location of a Work Place Affects
People’s Travel. Paper presented at The 12th International Conference of Hong Kong Society
for Transportation Studies, Hong Kong, December, 2007.
7. Krizek, KJ. A pre-test/post-test strategy for researching neighbourhood-scale urban form
and travel behaviour. J Transport Res Board No. 1722, 2000. pp. 48-55.
8. Cao, Xinyu, Patricia L. Mokhtarian, and Susan L. Handy. Examining The Impacts of
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Institute of Transportation Studies, University of California, Daivs, Research report UCDITS-RR-08-25, 2008.
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Toward a better understanding of land use and travel.’ Transportation Research Record, n
1780, 2001, p 64-75.
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Trip Energy Consumption. International Journal of Sustainable Transportation, Vol. 2, No.
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Oxford, England: Transport Studies Unit, Oxford University, Ref. 119/PR, 1980.
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1982, pp.51-69.
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15. Clarke, M. I. Dix, M.C. and Goodwin, P. Some issues of dynamics in forecasting travel
behavior—a discussed paper. Transportation, 11, 1982, pp. 153-172.
16. Waygood, E.O.D. and R. Kitamura. Children in a Rail-Based Developed Area of Japan:
Travel Patterns, Independence, and Exercise. TRB Annual Meeting 2009 CD-ROM..
17. Baron, R.A., D. Byrne, and G. Watson. Exploring Social Pschology. Pearson Education
Canada Inc., Toronto, 2005. pp. 254.
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and Walking Trips in Ten U.S. Metropolitan Areas. American Journal of Preventative
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Sun, Waygood, Fukui, and Kitamura
11
List of Tables
TABLE 1 Factors Used to Distinguish Different Developed Areas
TABLE 2 The Number of Distinct Developed Areas for the Years 1970, 1980, 1990, and
2000
TABLE 3 Characteristics of the five built environment areas.
TABLE 4 Descriptions and Definitions of Lifecycle Stages
TABLE 5 Results of ANOVA Analysis on the Fraction of Automobile Trips per
Household for the Year 2000
List of Figures
FIGURE 1 Change in the fraction of automobile trips per household across different
developed areas.
FIGURE 2 A comparison of the fraction of all trips that are by automobile for different
household lifecycle stages across distinct developed areas for the year 2000.
FIGURE 3 The number of all trips for each household by lifecycle stage across different
developed areas.
Sun, Waygood, Fukui, and Kitamura
12
TABLE 1 Factors Used to Distinguish Different Developed Areas
Division
①Population
Factor
Population density (people/km2)
Change in population between day and night
(%)
Population concentration (%)
②Commercial
Office density (office/km2)
Retail shop density (shops/km2)
Super market density (shops/km2)
Service density (shops/km2)
③Employment
Employment rate change (%)
Employment rate (%)
Commuter percentage (%)
④Household
Single person household percentage
Average household size (people/household)
⑤Individuals
Average age (years)
Elderly percentage
Youth percentage
⑥Private
automobile
ownership
Average household ownership
(vehicles/household)
Source
National census
National census and
Municipal statistics
National census
Report on offices
and enterprises
National manpower,
Census of
Commerce
National manpower，
Supermarket
directory
Report on offices
and enterprises
National census
National census
National census and
Municipal statistics
National census and
Municipal statistics
National census and
Municipal statistics
National census
National census and
National manpower
National census and
Municipal manpower
Municipal Household
Vehicle Ownership
Registration
Sun, Waygood, Fukui, and Kitamura
13
TABLE 2 The Number of Distinct Developed Areas for the Years 1970, 1980, 1990, and
2000
1970
1980
1990
2000
5
7
8
7
Commercial
23
26
27
31
Mixed commercial-residential
Mixed residential
Autonomous
Unurbanized area
45
98
23
103
26
19
110
27
4
110
31
12
Sun, Waygood, Fukui, and Kitamura
14
TABLE 3 Characteristics of the five built environment areas.
Highly
Commercial
Mixed
Commercial
Mixed
Residential
Autonomous
Undeveloped
Household
size
1 to 10
1 to 13
1 to 13
1 to 10
1 to 9
2.2
2.5
2.9
3.1
3.3
2
2
3
3
3
HH cars
0 to 12
0 to 11
0 to 15
0 to 12
0 to 15
0.49
0.623
1.11
1.63
1.82
0
0
1
1
2
0 to 10
0 to 8
0 to 11
0 to 7
0 to 6
0.15
0.17
0.3
0.45
0.5
0
0
0
0
0
0 to 7
0 to 9
0 to 9
0 to 9
0 to 9
1.13
1.44
1.34
1.36
0.92
1
1
1
1
0
HH
motorcycles
HH bicycles
Population
Density
(people/km2)
Service
Density
(businesses/
km2)
4224 to 12594
5493 to 18757
48 to 16114
74 to 2457
35 to 1976
8985 (36/acre)
12620 (51/acre)
3770 (15/acre)
1138 (4.6/acre)
493 (2.0/acre)
7717 (31/acre)
11934 (48/acre)
3191(13/acre)
843 (3.4/acre)
373 (1.5/acre)
222 to 1137
97 to 776
0 to 208
0.6 to 25
0.5 to 15
558 (2.3/acre)
189 (0.76/acre)
38.1 (0.15/acre)
15 (0.06/acre)
4 (0.02/acre)
339 (1.4/acre)
163 (0.66/acre)
31.2 (0.13/acre)
12 (0.05/acre)
2 (0.01/acre)
Sun, Waygood, Fukui, and Kitamura
15
TABLE 4 Descriptions and Definitions of Lifecycle Stages
A
B
Acronym
Younger single
Younger childless couple
Descriptions
Younger single household
Younger childless-couple household
Nuclear families with pre-school
children
Nuclear families with young school
children
Nuclear families with older school
children
C
Pre-school nuclear
D
Young school nuclear
E
Older school nuclear
F
All adults
Families of all adults
G
H
I
Older childless couple
Older single
Single parent
Older childless-couple household
Older single household
Single-parent household
J
Others
Other households
Definitions
Single adult with age under 60
Oldest person under 60
Youngest child under 6
Youngest child 6 or over but
under 12
Youngest child 12 or over but
under 18
Nuclear families and single
parent families with all
members of working age
Oldest person 60 or over
Age 60 or over
Youngest child under 18
Families with three generation,
other related persons and
unrelated persons
Sun, Waygood, Fukui, and Kitamura
100%
16
Highly Commercial Area
Mixed Commercial Area
Mixed Residential Area
Autonomous Area
Unurbanized area
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1965
1975
1985
1995
2005
FIGURE 1 Change in the fraction of automobile trips per household across different
developed areas.
Sun, Waygood, Fukui, and Kitamura
70.0%
17
Highly Commercial
area
60.0%
50.0%
40.0%
Mixed commercialresidential area
Mixed Residential
Area
30.0%
20.0%
10.0%
Unurbanized area
Autonomous area
Yo
un
g
ch
er
Yo
un
g
er
Pr ildl sin
e e
gl
Yo -sc ss
e
c
h
un
oo ou
p
g
s l n le
O
ld ch uc
er oo le
sc l n ar
ho uc
ol lea
O
nu r
ld
er
c
ch Al lea
l
ild
ad r
le
ss ults
O cou
ld
e p
Si r s le
ng in
le gle
pa
re
O nt
th
er
s
0.0%
FIGURE 2 A comparison of the fraction of all trips that are by automobile for different
household lifecycle stages across distinct developed areas for the year 2000.
Sun, Waygood, Fukui, and Kitamura
18
TABLE 5 Results of ANOVA Analysis on the Fraction of Automobile Trips per
Household for the Year 2000
Sum of Squares
Corrected Model
Intercept
Built Environment (BE)
Lifecycle stage (LCS)
LCS x BE
Error
Total
Corrected Total
*=significant at α=0.05
N = 146,820
1510.8
794.6
577.5
58.0
45.9
11685.5
21551.7
13196.3
Degrees of
Freedom
49
1
4
9
36
110146
110196
110195
Mean Square
F
30.8
794.6
144.4
6.4
1.3
0.11
290.6*
7490.2*
1360.8*
60.7*
12.0*
Sun, Waygood, Fukui, and Kitamura
19
Highly Commercial area
Mixed Commercial-Residential area
Mixed Residential-Commercial
Unurbanized area
Autonomous area
14.00
12.00
10.00
8.00
6.00
4.00
2.00
er
s
O
th
t
re
n
le
pa
sin
Si
ng
er
O
ld
co
up
gl
e
le
lts
ss
ch
ild
le
Al
la
du
le
a
er
O
ld
er
sc
ho
o
ln
uc
cl
nu
ho
ol
sc
un
g
Yo
r
ea
r
ar
cle
nu
ho
ol
s
le
s
ild
Pr
esc
O
ld
Yo
un
g
er
ch
Yo
un
g
er
sin
co
up
gl
e
le
0.00
FIGURE 3 The number of all trips for each household by lifecycle stage across different
developed areas.